Index
- Randomized Response + Bloom Filter
- Discussed how to train deep neural networks with non-convex objectives, under a modest privacy budget
- Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning* - Hitaj et al., CCS '17
- Proposed and implement an active inference attack on deep neural networks in a collaborative setting(which stresses the importance of using secure aggregation and differential privacy.)
- Prio: Private, Robust, and Scalable Computation of Aggregate Statistics - Corrigan-Gibbs et al., NSDI '17
- Honeycrisp: Large-Scale Differentially Private Aggregation Without a Trusted Core - Roth et al., SOSP '19
- Shredder: Learning Noise Distributions to Protect Inference Privacy - Mireshghallah et al., ASPLOS ' 20
Last modified 1yr ago